SoroushVahidi/scidocs huggingface dataset:
Quiet Neighborhoods: Key to Protect Job Performance Predictability
(2015)

Interference of nearby jobs has been recently identified as the dominant reason for the high performance variability of parallel applications running on High Performance Computing (HPC) systems. Typically, HPC systems are dynamic with multiple jobs coming and leaving in an unpredictable fashion, sharing simultaneously the system interconnection network. In such environment contention for network resources is causing random stalls in the progress of application execution degrading application and system performance overall. Eliminating job interactions in their neighbourhoods is key for guaranteeing performance predictability of applications. In this paper we are proposing the concept of quiet neighbourhoods that significantly reduce job interactions. Quiet neighbourhoods are created by the system resource manager in two phases. First, multiple virtual network blocks are defined on the top of the physical network resources based on typical workload distributions. Second, newly arriving jobs are allocated in these virtual blocks based on their size.
Use of platelet-rich fibrin membrane following treatment of gingival recession: a randomized clinical trial.
(2012)

This 6-month randomized controlled clinical study primarily aimed to compare the results achieved by the use of a platelet-rich fibrin (PRF) membrane or connective tissue graft (CTG) in the treatment of gingival recession and to evaluate the clinical impact of PRF on early wound healing and subjective patient discomfort. Use of a PRF membrane in gingival recession treatment provided acceptable clinical results, followed by enhanced wound healing and decreased subjective patient discomfort compared to CTG-treated gingival recessions. No difference could be found between PRF and CTG procedures in gingival recession therapy, except for a greater gain in keratinized tissue width obtained in the CTG group and enhanced wound healing associated with the PRF group.
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
(2015)

Deep Convolutional Neural Networks (DCNNs) have recently shown state of the art performance in high level vision tasks, such as image classification and object detection. This work brings together methods from DCNNs and probabilistic graphical models for addressing the task of pixel-level classification (also called ”semantic image segmentation”). We show that responses at the final layer of DCNNs are not sufficiently localized for accurate object segmentation. This is due to the very invariance properties that make DCNNs good for high level tasks. We overcome this poor localization property of deep networks by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF). Qualitatively, our “DeepLab” system is able to localize segment boundaries at a level of accuracy which is beyond previous methods. Quantitatively, our method sets the new state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching 71.6% IOU accuracy in the test set. We show how these results can be obtained efficiently: Careful network re-purposing and a novel application of the ’hole’ algorithm from the wavelet community allow dense computation of neural net responses at 8 frames per second on a modern GPU.
PhaseNet: A Deep Convolutional Neural Network for Two-Dimensional Phase Unwrapping
(2019)

Phase unwrapping is a crucial signal processing problem in several applications that aims to restore original phase from the wrapped phase. In this letter, we propose a novel framework for unwrapping the phase using deep fully convolutional neural network termed as PhaseNet. We reformulate the problem definition of directly obtaining continuous original phase as obtaining the wrap-count (integer jump of 2 $\pi$) at each pixel by semantic segmentation and this is accomplished through a suitable deep learning framework. The proposed architecture consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The relationship between the absolute phase and the wrap-count is leveraged in generating abundant simulated data of several random shapes. This deliberates the network on learning continuity in wrapped phase maps rather than specific patterns in the training data. We compare the proposed framework with the widely adapted quality-guided phase unwrapping algorithm and also with the well-known MATLAB's unwrap function for varying noise levels. The proposed framework is found to be robust to noise and computationally fast. The results obtained highlight that deep convolutional neural network can indeed be effectively applied for phase unwrapping, and the proposed framework will hopefully pave the way for the development of a new set of deep learning based phase unwrapping methods.
Why women use makeup: implication of psychological traits in makeup functions.
(2008)

Makeup acts and stimulates three of our senses: touch (which encompasses all sensations from the body surface), smell (fragrance), and sight (the process of becoming and looking beautiful). The positive stimulation of these senses by makeup can induce sensory as well as psychological pleasure. In order to understand the relationship of women to their makeup, we interviewed different groups of women on their quality of life and makeup habits. Then, through four standard well-validated psychometric self-questionnaires, we examined the possible relation between the need to make up oneself and specific psychological features. Our first results clearly showed that makeup could support two opposite "up" functions, i.e., "camouflage" vs "seduction." Concerning their psychological profiles, results showed that women of the functional class "camouflage" are more anxious, defensive, and emotionally unstable compared to those of the functional class "seduction," who appear to be more sociable, assertive, and extroverted. Further analyses revealed a division of the two classes into subclasses of volunteers with opposed personality and psychological profiles. This new classification allowed us to define more precisely the relations existing within the subjective experience of women during the makeup process. In conclusion, our study revealed that beyond the simple application of colorful products on the face, makeup has two major functional implications depending on specific psychological profiles of women.
Measuring the Physical in Physical Attractiveness : Quasi-Experiments on the Sociobiology of Female Facial Beauty
(2004)

Two quasi-experiments investigated the relation between specific adult female facial features and the attraction, attribution, and altruistic responses of adult males. Precise measurements were obtained of the relative size of 24 facial features in an international sample of photographs of 50 females. Male subjects provided ratings of the attractiveness of each of the females. Positively correlated with attractiveness ratings were the neonate features of large eyes, small nose, and small chin; the maturity features of prominent cheekbones and narrow cheeks; and the expressive features of high eyebrows, large pupils, and large smile. A second study asked males to rate the personal characteristics of 16 previously measured females. The males were also asked to indicate the females for whom they would be most inclined to perform altruistic behaviors, and select for dating, sexual behavior, and childrearing. The second study replicated the correlations of feature measurements with attractiveness. Facial features also predicted personality attributions, altruistic inclinations, and reproductive interest. Sociobiological interpretations are discussed.
Women's use of cosmetics: psychosocial correlates and consequences.
(1982)

Synopsis Few systematic studies have examined individual differences in women's use of facial make-up or the possible psychosocial effects of such use. In the present investigation, the researchers developed several measures of the amount and the situational and temporal patterning of cosmetics use. Among forty-two female college students, differential use was associated with a number of selected personality variables-public self-consciousness, public body-consciousness, social anxiety, and various body-image factors. Through the imagery-induction of cosmetics use and non-use conditions, cosmetics users' self-evaluative responses were studied. In a variety of imagined situations, subjects reported being more self-confident and sociable when wearing as opposed to not wearing their customary cosmetics. Findings were discussed in the context of the role of cosmetics in self-image enhancement and social impression management.
Neuroplasticity in the context of motor rehabilitation after stroke
(2011)

Approximately one-third of patients with stroke exhibit persistent disability after the initial cerebrovascular episode, with motor impairments accounting for most poststroke disability. Exercise and training have long been used to restore motor function after stroke. Better training strategies and therapies to enhance the effects of these rehabilitative protocols are currently being developed for poststroke disability. The advancement of our understanding of the neuroplastic changes associated with poststroke motor impairment and the innate mechanisms of repair is crucial to this endeavor. Pharmaceutical, biological and electrophysiological treatments that augment neuroplasticity are being explored to further extend the boundaries of poststroke rehabilitation. Potential motor rehabilitation therapies, such as stem cell therapy, exogenous tissue engineering and brain–computer interface technologies, could be integral in helping patients with stroke regain motor control. As the methods for providing motor rehabilitation change, the primary goals of poststroke rehabilitation will be driven by the activity and quality of life needs of individual patients. This Review aims to provide a focused overview of neuroplasticity associated with poststroke motor impairment, and the latest experimental interventions being developed to manipulate neuroplasticity to enhance motor rehabilitation.
Learning personal + social latent factor model for social recommendation
(2012)

Social recommendation, which aims to systematically leverage the social relationships between users as well as their past behaviors for automatic recommendation, attract much attention recently. The belief is that users linked with each other in social networks tend to share certain common interests or have similar tastes (homophily principle); such similarity is expected to help improve the recommendation accuracy and quality. There have been a few studies on social recommendations; however, they almost completely ignored the heterogeneity and diversity of the social relationship.
 In this paper, we develop a joint personal and social latent factor (PSLF) model for social recommendation. Specifically, it combines the state-of-the-art collaborative filtering and the social network modeling approaches for social recommendation. Especially, the PSLF extracts the social factor vectors for each user based on the state-of-the-art mixture membership stochastic blockmodel, which can explicitly express the varieties of the social relationship. To optimize the PSLF model, we develop a scalable expectation-maximization (EM) algorithm, which utilizes a novel approximate mean-field technique for fast expectation computation. We compare our approach with the latest social recommendation approaches on two real datasets, Flixter and Douban (both with large social networks). With similar training cost, our approach has shown a significant improvement in terms of prediction accuracy criteria over the existing approaches.
The Implications and Impacts of Web Services to Electronic Commerce Research and Practices
(2003)

Web services refer to a family of technologies that can universally standardize the communication of applications in order to connect systems, business partners, and customers cost-effectively through the World Wide Web. Major software vendors such as IBM, Microsoft, SAP, SUN, and Oracle are all embracing Web services standards and are releasing new products or tools that are Web services enabled. Web services will ease the constraints of time, cost, and space for discovering, negotiating, and conducting e-business transactions. As a result, Web services will change the way businesses design their applications as services, integrate with other business entities, manage business process workflows, and conduct e-business transactions. The early adopters of Web services are showing promising results such as greater development productivity gains and easier and faster integration with trading partners. However, there are many issues worth studying regarding Web services in the context of e-commerce. This special issue of the JECR aims to encourage awareness and discussion of important issues and applications of Web Services that are related to electronic commerce from the organizational, economics, and technical perspectives. Research opportunities of Web services and e-commerce area are fruitful and important for both academics and practitioners. We wish that this introductory article can shed some light for researchers and practitioners to better understand important issues and future trends of Web services and e-business.
Social stories: mechanisms of effectiveness in increasing game play skills in children diagnosed with autism spectrum disorder using a pretest posttest repeated measures randomized control group design.
(2009)

An increasing body of literature has indicated that social stories are an effective way to teach individuals diagnosed with autism appropriate social behavior. This study compared two formats of a social story targeting the improvement of social skills during game play using a pretest posttest repeated measures randomized control group design. A total of 45 children diagnosed with Autism Spectrum Disorder (ASD) ages 7-14 were randomly assigned to standard, directive, or control story conditions. Results demonstrated that the standard and directive story formats were equally as effective in eliciting, generalizing and maintaining the targeted social skills in participants who had prior game play experience and Verbal Comprehension Index (VCI) scores from the WISC-IV intelligence test in the borderline range or above.
What is a robot companion - friend, assistant or butler?
(2005)

The study presented in this paper explored people's perceptions and attitudes towards the idea of a future robot companion for the home. A human-centred approach was adopted using questionnaires and human-robot interaction trials to derive data from 28 adults. Results indicated that a large proportion of participants were in favour of a robot companion and saw the potential role as being an assistant, machine or servant. Few wanted a robot companion to be a friend. Household tasks were preferred to child/animal care tasks. Humanlike communication was desirable for a robot companion, whereas humanlike behaviour and appearance were less essential. Results are discussed in relation to future research directions for the development of robot companions.
Narrative Visualization: Sharing Insights into Complex Data
(2012)

This paper is a reflection on the emerging genre of narrative visualization, a creative response to the need to share complex data engagingly with the public. In it, we explain how narrative visualization offers authors the opportunity to communicate more effectively with their audience by reproducing and sharing an experience of insight similar to their own. To do so, we propose a two part model, derived from previous literature, in which insight is understood as both an experience and also the product of that experience. We then discuss how the design of narrative visualization should be informed by attempts elsewhere to track the provenance of insights and share them in a collaborative setting. Finally, we present a future direction for research that includes using EEG technology to record neurological patterns during episodes of insight experience as the basis for evaluation.
Realistic and Fast Cloud Rendering
(2004)

Clouds are an important aspect of rendering outdoor scenes. Thi paper describes a cloud system that extends texture splatting on par ticles to model a dozen cloud types (e.g., stratus, cumulus congestus, cumulonimb us), an improvement over earlier systems that modeled only one type of cumulus. W e also achieve fast real-time rendering, even for scenes of dense overcast cove rage, which was a limitation for previous systems. We present a new shading model that uses artist-driven contr ols rather than a programmatic approach to approximate lighting. This is sui table when fine-grained control over the look-and-feel is necessary, and artistic r esources are available. We also introduce a way to simulate cloud formation and dissipa tion using texture
FaceTube: predicting personality from facial expressions of emotion in online conversational video
(2012)

The advances in automatic facial expression recognition make possible to mine and characterize large amounts of data, opening a wide research domain on behavioral understanding. In this paper, we leverage the use of a state-of-the-art facial expression recognition technology to characterize users of a popular type of online social video, conversational vlogs. First, we propose the use of several activity cues to characterize vloggers based on frame-by-frame estimates of facial expressions of emotion. Then, we present results for the task of automatically predicting vloggers' personality impressions using facial expressions and the Big-Five traits. Our results are promising, specially for the case of the Extraversion impression, and in addition our work poses interesting questions regarding the representation of multiple natural facial expressions occurring in conversational video.
Minutiae Extraction From Level 1 Features of Fingerprint
(2016)

Fingerprint features can be divided into three major categories based on the granularity at which they are extracted: level 1, level 2, and level 3 features. Orientation field, ridge frequency field, and minutiae set are three fundamental components of fingerprint, where the orientation field and ridge frequency field are regarded as level 1 features and minutiae set as level 2 features. It is generally believed that level 1 features, especially orientation field, can be reconstructed from level 2 features, i.e., minutiae. However, it is still a question that if minutiae can be extracted from level 1 features. In this paper, we analyze the relations between level 1 and level 2 features using the frequency modulation (FM) model and propose an approach to extract minutiae from level 1 features (i.e., orientation field and frequency field). The proposed algorithm is evaluated on NIST SD27 and FVC2002 DB1 databases. The true detection rate (TDR) and false detection rate (FDR) of minutiae detection on NIST SD27 and FVC2002 DB1 are about 45% and 30% compared with manually marked minutiae, respectively, with level 1 features extracted at a block size of 16 pixels. When pixelwise orientation and frequency fields are available, TDR and FDR can reach 70% and 25%, respectively. With a smaller block size, the minutiae recovering accuracy can be even higher. Our quantitative and experimental results show the deep relationship between level 1 and level 2 features of a fingerprint.
Single image tree modeling
(2008)

In this paper, we introduce a simple sketching method to generate a realistic 3D tree model from a single image. The user draws at least two strokes in the tree image: the first crown stroke around the tree crown to mark up the leaf region, the second branch stroke from the tree root to mark up the main trunk, and possibly few other branch strokes for refinement. The method automatically generates a 3D tree model including branches and leaves. Branches are synthesized by a growth engine from a small library of elementary subtrees that are pre-defined or built on the fly from the recovered visible branches. The visible branches are automatically traced from the drawn branch strokes according to image statistics on the strokes. Leaves are generated from the region bounded by the first crown stroke to complete the tree. We demonstrate our method on a variety of examples.
Effects of manual lymphatic drainage on breast cancer-related lymphedema: a systematic review and meta-analysis of randomized controlled trials
(2013)

BACKGROUND
Lymphedema is a common complication of axillary dissection for breast cancer. We investigated whether manual lymphatic drainage (MLD) could prevent or manage limb edema in women after breast-cancer surgery.


METHODS
We performed a systematic review and meta-analysis of published randomized controlled trials (RCTs) to evaluate the effectiveness of MLD in the prevention and treatment of breast-cancer-related lymphedema. The PubMed, EMBASE, CINAHL, Physiotherapy Evidence Database (PEDro), SCOPUS, and Cochrane Central Register of Controlled Trials electronic databases were searched for articles on MLD published before December 2012, with no language restrictions. The primary outcome for prevention was the incidence of postoperative lymphedema. The outcome for management of lymphedema was a reduction in edema volume.


RESULTS
In total, 10 RCTs with 566 patients were identified. Two studies evaluating the preventive outcome of MLD found no significant difference in the incidence of lymphedema between the MLD and standard treatment groups, with a risk ratio of 0.63 and a 95% confidence interval (CI) of 0.14 to 2.82. Seven studies assessed the reduction in arm volume, and found no significant difference between the MLD and standard treatment groups, with a weighted mean difference of 75.12 (95% CI, -9.34 to 159.58).


CONCLUSIONS
The current evidence from RCTs does not support the use of MLD in preventing or treating lymphedema. However, clinical and statistical inconsistencies between the various studies confounded our evaluation of the effect of MLD on breast-cancer-related lymphedema.